Facial-based intrusion detection system with deep learning in embedded devices

With the advent of deep learning based methods, facial recognition algorithms have become more effective and efficient. However, these algorithms have usually the disadvantage of requiring the use of dedicated hardware devices, such as graphical processing units (GPUs), which pose restrictions on their usage on embedded devices with limited computational power. In this paper, we present an approach that allows building an intrusion detection system, based on face recognition, running on embedded devices. It relies on deep learning techniques and does not exploit the GPUs. Face recognition is performed using a knn classifier on features extracted from a 50-layers Residual Network (ResNet-50) trained on the VGGFace2 dataset. In our experiment, we determined the optimal confidence threshold that allows distin- guishing legitimate users from intruders. In order to validate the proposed system, we created a ground truth composed of 15,393 images of faces and 44 identities, captured by two smart cameras placed in two different offices, in a test period of six months. We show that the obtained results are good both from the efficiency and effectiveness perspective.